用物理约束的流模型提升磁共振波谱代谢物定量精度与可信度
Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy
- 基于物理先验的解码器结合斯维尔特流，建模代谢物浓度后验分布
- 在7T模拟数据上实现高精度量化与校准的不确定性估计
- 适合需要可靠定量与不确定性分析的医学影像研究者
磁共振波谱（MRS）是一种无创技术，可测量组织的代谢组成，对神经疾病、肿瘤检测等具有重要价值。但代谢物定量受谱峰重叠、信噪比低及多种伪影影响。传统线性叠加模型存在歧义，且仅能提供理论误差下限（Cramér-Rao界）。本文提出基于斯维尔特归一化流（SNF）的贝叶斯推断框架，用于近似代谢物浓度的后验分布。引入物理驱动的解码器，融合信号生成先验知识，确保分布表示真实可信。在7T质子MRS模拟数据上验证，方法实现了精确的代谢物定量、校准的不确定性估计，并揭示了参数相关性与多模态分布特性。
原文摘要 · Abstract (English)
Magnetic resonance spectroscopy (MRS) is a non-invasive technique to measure the metabolic composition of tissues, offering valuable insights into neurological disorders, tumor detection, and other metabolic dysfunctions. However, accurate metabolite quantification is hindered by challenges such as spectral overlap, low signal-to-noise ratio, and various artifacts. Traditional methods like linear-combination modeling are susceptible to ambiguities and commonly only provide a theoretical lower bound on estimation accuracy in the form of the Cramér-Rao bound. This work introduces a Bayesian inference framework using Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations, enhancing quantification reliability. A physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations. We validate the method on simulated 7T proton MRS data, demonstrating accurate metabolite quantification, well-calibrated uncertainties, and insights into parameter correlations and multi-modal distributions.
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